Smart environments have been a popular research topic during the last decade. Smart spaces (also labeled as pervasive computing environments, smart environments, or spaces with ambient intelligence) are technologically augmented work environments that are used by groups of users, who work collaboratively on a task. For instance, a workplace meeting room can be equipped as a smart space to gather metadata about activities taking place there, such as user (e.g., meeting attendee) locations and active speaker detection. The gathered information can provide rich feedback to the user, such as information for later review of the meeting or creating video summaries etc.
Previous work on egocentric tracking in smart spaces has accomplished user localization by instrumenting smart spaces with an extensive array of sensors, such as depth sensors and cameras. However, such conventional methods have limitations, e.g., the scalability to very large rooms and/or the ability to track a large number of participants. In particular, conventional methods often require exponentially more sensors for scalability. Thus, when using conventional methods to track a large crowd, occlusion problems ensue.